Control Techniques for Complex Networks (Pre-publication Version)

This book gives the tools and philosophy you need to build network models detailed enough to capture essential dynamics but simple enough to expose the structure of effective control solutions and to clarify analysis.

**Publication date**: 22 Apr 2007

**ISBN-10**:
0521884411

**ISBN-13**:
9780521884419

**Paperback**:
615 pages

**Views**: 10,587

Control Techniques for Complex Networks (Pre-publication Version)

This book gives the tools and philosophy you need to build network models detailed enough to capture essential dynamics but simple enough to expose the structure of effective control solutions and to clarify analysis.

From the Preface:

What’s in here?

The book is divided into three parts. The first part entitled modeling and control contains numerous examples to illustrate some of the basic concepts developed in the book, especially those topics listed in (i) and (ii) concerning the fluid and CRW models. Lyapunov functions and the dynamic programming equations are introduced; Based on these concepts we arrive at the Max Weight policy along with many generalizations.

Workload relaxations arc introduced in Part II. In these three chapters we show how a cost function defined for the network can be ‘projected’ to define the effective cost for the relaxation. Applications to control involve first constructing a policy for the low-dimensional relaxation, and then translating this to the original physical system of interest. This translation step involves the introduction of hedging to guard against variability.

Most of the control techniques are contained in the first two parts of the book. Part III entitled Stability & Performance contains an in-depth treatment of Lyapunov stability theory and optimization. It contains approximation techniques to explain the apparent solidarity between control solutions for stochastic and deterministic network models. Moreover, this part of the book develops several approaches to performance evaluation for stochastic network models.

Tweet

About The Author(s)

Professor and Robert C. Pittman Eminent Scholar Chair in the Department of Electrical and Computer Engineering at the University of Florida, the director of the Laboratory for Cognition & Control, and director of the Florida Institute for Sustainable Energy. Academic research interests include theory and applications of decision and control, stochastic processes, and optimization.

Book Categories

Computer Science
Introduction to Computer Science
Introduction to Computer Programming
Algorithms and Data Structures
Artificial Intelligence
Computer Vision
Machine Learning
Neural Networks
Game Development and Multimedia
Data Communication and Networks
Coding Theory
Computer Security
Information Security
Cryptography
Information Theory
Computer Organization and Architecture
Operating Systems
Image Processing
Parallel Computing
Concurrent Programming
Relational Database
Document-oriented Database
Data Mining
Big Data
Data Science
Digital Libraries
Compiler Design and Construction
Functional Programming
Logic Programming
Object Oriented Programming
Formal Methods
Software Engineering
Agile Software Development
Information Systems
Geographic Information System (GIS)

Mathematics
Mathematics
Algebra
Abstract Algebra
Linear Algebra
Number Theory
Numerical Methods
Precalculus
Calculus
Differential Equations
Category Theory
Proofs
Discrete Mathematics
Theory of Computation
Graph Theory
Real Analysis
Complex Analysis
Probability
Statistics
Game Theory
Queueing Theory
Operations Research
Computer Aided Mathematics

Supporting Fields
Web Design and Development
Mobile App Design and Development
System Administration
Cloud Computing
Electric Circuits
Embedded System
Signal Processing
Integration and Automation
Network Science
Project Management

Operating System
Programming/Scripting
Ada
Assembly
C / C++
Common Lisp
Forth
Java
JavaScript
Lua
Rexx
Microsoft .NET
Perl
PHP
R
Python
Rebol
Ruby
Scheme
Tcl/Tk

Miscellaneous
Sponsors